In this course, you will learn the fundamental techniques for making personalized recommendations through nearest-neighbor techniques. First you will learn user-user collaborative filtering, an algorithm that identifies other people with similar tastes to a target user and combines their ratings to make recommendations for that user. You will explore and implement variations of the user-user algorithm, and will explore the benefits and drawbacks of the general approach. Then you will learn the widely-practiced item-item collaborative filtering algorithm, which identifies global product associations from user ratings, but uses these product associations to provide personalized recommendations based on a user's own product ratings.
Este curso faz parte do Programa de cursos integrados Sistemas de recomendação
Nearest Neighbor Collaborative Filtering
oferecido por
Programa de cursos integrados Sistemas de recomendaçãoUniversidade de MinnesotaUniversidade de Minnesota
Os alunos que estão fazendo este Course são
- Data Scientists
- Machine Learning Engineers
- Data Engineers
- Business Analysts
- Research Assistants
Programa - O que você aprenderá com este curso
Semana
113 minutos para concluir
Preface
Note that this course is structured into two-week chunks. The first chunk focuses on User-User Collaborative Filtering; the second chunk on Item-Item Collaborative Filtering. Each chunk has most of the lectures in the first week, and assignments/quizzes and advanced topics in the second week. We encourage learners to treat each two-week chunk as one unit, starting the assignments as soon as they feel they have learned enough to get going.
1 vídeo (Total 3 mín.), 1 leitura
1 vídeos
1 leituras
Course Structure Outline10min
1 hora para concluir
User-User Collaborative Filtering Recommenders Part 1
5 vídeos (Total 85 mín.)
5 videos
Configuring User-User Collaborative Filtering9min
Influence Limiting and Attack Resistance; Interview with Paul Resnick21min
Trust-Based Recommendation; Interview with Jen Golbeck15min
Impact of Bad Ratings; Interview with Dan Cosley13min
Semana
25 horas para concluir
User-User Collaborative Filtering Recommenders Part 2
2 vídeos (Total 13 mín.), 2 leituras, 3 testes
2 videos
Programming Assignment - Programming User-User Collaborative Filtering4min
2 leituras
Assignment Instructions: User-User CF10min
Introducing User-User CF Programming Assignment10min
2 exercícios práticos
User-User CF Answer Sheet48min
User-User Collaborative Filtering Quiz20min
Semana
31 hora para concluir
Item-Item Collaborative Filtering Recommenders Part 1
6 vídeos (Total 70 mín.)
6 videos
Item-Item Algorithm16min
Item-Item on Unary Data6min
Item-Item Hybrids and Extensions4min
Strengths and Weaknesses of Item-Item Collaborative Filtering9min
Interview with Brad Miller16min
Semana
44 horas para concluir
Item-Item Collaborative Filtering Recommenders Part 2
2 vídeos (Total 10 mín.), 2 leituras, 5 testes
2 videos
Programming Assignment - Programming Item-Item Collaborative Filtering4min
2 leituras
Item-Based CF Assignment Instructions10min
Introducing Item-Item CF Programming Assignment10min
4 exercícios práticos
Item Based Assignment Part l10min
Item Based Assignment Part II10min
Item Based Assignment Part III10min
Item Based Assignment Part IV10min
2 horas para concluir
Advanced Collaborative Filtering Topics
5 vídeos (Total 73 mín.), 1 teste
5 videos
Recommending for Groups: Interview with Anthony Jameson14min
Threat Models11min
Explanations16min
Explanations, Part II: Interview with Nava Tintarev17min
1 exercício prático
Item-Based and Advanced Collaborative Filtering Topics Quiz20min
Principais avaliações do Nearest Neighbor Collaborative Filtering
Thank you so very much to open my eye see more view of recommendation field not only algorithms but use case and many trouble-shooting in worldwide business, moreover interview with noble professor.
Extremely informative course! It would be great if the assignments are created on python or R in the next season's offering. Thanks for the knowledge!
Instrutores
Joseph A Konstan
Distinguished McKnight Professor and Distinguished University Teaching ProfessorComputer Science and Engineering
Michael D. Ekstrand
Assistant ProfessorDept. of Computer Science, Boise State University
Sobre Universidade de MinnesotaUniversidade de Minnesota
The University of Minnesota is among the largest public research universities in the country, offering undergraduate, graduate, and professional students a multitude of opportunities for study and research. Located at the heart of one of the nation’s most vibrant, diverse metropolitan communities, students on the campuses in Minneapolis and St. Paul benefit from extensive partnerships with world-renowned health centers, international corporations, government agencies, and arts, nonprofit, and public service organizations.
Sobre Programa de cursos integrados Sistemas de recomendação
A Recommender System is a process that seeks to predict user preferences. This Specialization covers all the fundamental techniques in recommender systems, from non-personalized and project-association recommenders through content-based and collaborative filtering techniques, as well as advanced topics like matrix factorization, hybrid machine learning methods for recommender systems, and dimension reduction techniques for the user-product preference space.
This Specialization is designed to serve both the data mining expert who would want to implement techniques like collaborative filtering in their job, as well as the data literate marketing professional, who would want to gain more familiarity with these topics.
The courses offer interactive, spreadsheet-based exercises to master different algorithms, along with an honors track where you can go into greater depth using the LensKit open source toolkit.
By the end of this Specialization, you’ll be able to implement as well as evaluate recommender systems. The Capstone Project brings together the course material with a realistic recommender design and analysis project.
Perguntas Frequentes – FAQ
Quando terei acesso às palestras e às tarefas?
Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.
O que recebo ao me inscrever nesta Especialização?
Quando você se inscreve no curso, tem acesso a todos os cursos na Especialização e pode obter um certificado quando concluir o trabalho. Seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.
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